index int32 0 3.13k | question stringlengths 23 1.87k | answer stringclasses 8
values | A stringlengths 1 1.17k | B stringlengths 0 1.17k | C stringlengths 0 1.16k | D stringlengths 0 1.17k | E stringclasses 15
values | F stringclasses 6
values | G stringclasses 5
values | H stringclasses 4
values | I stringclasses 3
values | image images listlengths 1 9 | category stringclasses 162
values | l2-category stringclasses 32
values | split stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
200 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | A | [0.672, 0.012, 0.992, 0.976] | [0.672, 0.012, 0.94, 1.127] | [0.672, 0.012, 0.936, 0.97] | [0.512, 0.0, 0.832, 0.964] | referring_detection | visual_grounding | VAL | ||||||
201 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | B | [0.497, 0.113, 0.925, 0.277] | [0.553, 0.322, 0.752, 0.739] | [0.553, 0.322, 0.761, 0.784] | [0.553, 0.322, 0.761, 0.714] | referring_detection | visual_grounding | VAL | ||||||
202 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | C | [0.019, 0.628, 0.435, 0.734] | [0.463, 0.186, 0.492, 0.341] | [0.204, 0.164, 0.544, 0.991] | [0.217, 0.173, 0.556, 1.0] | referring_detection | visual_grounding | VAL | ||||||
203 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | A | [0.604, 0.357, 0.876, 0.584] | [0.676, 0.416, 0.948, 0.643] | [0.604, 0.357, 0.844, 0.587] | [0.604, 0.357, 0.836, 0.579] | referring_detection | visual_grounding | VAL | ||||||
204 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | D | [0.502, 0.342, 1.033, 0.523] | [0.418, 0.353, 0.668, 0.632] | [0.516, 0.307, 1.0, 0.461] | [0.502, 0.342, 0.985, 0.495] | referring_detection | visual_grounding | VAL | ||||||
205 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | D | [0.766, 0.64, 0.972, 0.975] | [0.109, 0.71, 0.234, 0.787] | [0.188, 0.469, 0.463, 0.938] | [0.116, 0.367, 0.391, 0.835] | referring_detection | visual_grounding | VAL | ||||||
206 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | A | [0.559, 0.296, 0.93, 0.602] | [0.03, 0.139, 0.127, 0.638] | [0.559, 0.296, 0.87, 0.546] | [0.63, 0.231, 1.0, 0.536] | referring_detection | visual_grounding | VAL | ||||||
207 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | A | [0.364, 0.155, 0.608, 0.989] | [0.364, 0.155, 0.61, 0.997] | [0.036, 0.48, 0.48, 0.624] | [0.486, 0.0, 0.73, 0.835] | referring_detection | visual_grounding | VAL | ||||||
208 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | C | [0.732, 0.472, 0.967, 0.548] | [0.031, 0.091, 0.561, 0.855] | [0.031, 0.091, 0.664, 0.88] | [0.031, 0.091, 0.709, 0.952] | referring_detection | visual_grounding | VAL | ||||||
209 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | D | [0.628, 0.0, 0.936, 0.715] | [0.324, 0.264, 0.348, 0.507] | [0.55, 0.061, 0.928, 0.411] | [0.692, 0.16, 1.0, 0.875] | referring_detection | visual_grounding | VAL | ||||||
210 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | B | [0.193, 0.662, 0.379, 1.0] | [0.261, 0.621, 0.448, 0.959] | [0.261, 0.621, 0.482, 1.016] | [0.261, 0.621, 0.438, 0.995] | referring_detection | visual_grounding | VAL | ||||||
211 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | C | [0.358, 0.338, 0.737, 0.526] | [0.186, 0.35, 0.531, 0.508] | [0.358, 0.338, 0.703, 0.497] | [0.358, 0.338, 0.719, 0.528] | referring_detection | visual_grounding | VAL | ||||||
212 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | A | [0.156, 0.153, 0.453, 0.622] | [0.117, 0.721, 0.245, 0.983] | [0.016, 0.029, 0.312, 0.498] | [0.65, 0.828, 0.697, 0.969] | referring_detection | visual_grounding | VAL | ||||||
213 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | D | [0.389, 0.537, 0.881, 0.917] | [0.005, 0.014, 0.963, 0.262] | [0.287, 0.495, 0.319, 0.845] | [0.005, 0.014, 0.816, 0.303] | referring_detection | visual_grounding | VAL | ||||||
214 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | C | [0.392, 0.422, 0.808, 0.78] | [0.003, 0.55, 1.112, 0.694] | [0.003, 0.55, 0.941, 0.688] | [0.003, 0.55, 1.032, 0.676] | referring_detection | visual_grounding | VAL | ||||||
215 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | C | [0.04, 0.498, 0.379, 0.692] | [0.614, 0.205, 0.916, 0.62] | [0.101, 0.178, 0.363, 0.388] | [0.101, 0.178, 0.398, 0.37] | referring_detection | visual_grounding | VAL | ||||||
216 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | D | [0.138, 0.485, 0.333, 0.829] | [0.205, 0.333, 0.422, 0.627] | [0.181, 0.415, 0.644, 0.623] | [0.205, 0.333, 0.4, 0.677] | referring_detection | visual_grounding | VAL | ||||||
217 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | D | [0.608, 0.481, 0.961, 0.779] | [0.484, 0.577, 0.947, 0.867] | [0.43, 0.554, 0.822, 0.85] | [0.608, 0.481, 1.0, 0.777] | referring_detection | visual_grounding | VAL | ||||||
218 | Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of th... | C | [0.634, 0.296, 0.94, 0.468] | [0.556, 0.299, 0.816, 0.489] | [0.556, 0.299, 0.862, 0.471] | [0.594, 0.381, 0.9, 0.553] | referring_detection | visual_grounding | VAL | ||||||
219 | Following the structural and analogical relations, which image best completes the problem matrix? | B | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
220 | Following the structural and analogical relations, which image best completes the problem matrix? | G | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
221 | Following the structural and analogical relations, which image best completes the problem matrix? | G | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
222 | Following the structural and analogical relations, which image best completes the problem matrix? | C | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
223 | Following the structural and analogical relations, which image best completes the problem matrix? | D | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
224 | Following the structural and analogical relations, which image best completes the problem matrix? | H | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
225 | Following the structural and analogical relations, which image best completes the problem matrix? | B | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
226 | Following the structural and analogical relations, which image best completes the problem matrix? | D | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
227 | Following the structural and analogical relations, which image best completes the problem matrix? | A | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
228 | Following the structural and analogical relations, which image best completes the problem matrix? | B | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
229 | Following the structural and analogical relations, which image best completes the problem matrix? | C | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
230 | Following the structural and analogical relations, which image best completes the problem matrix? | E | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
231 | Following the structural and analogical relations, which image best completes the problem matrix? | F | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
232 | Following the structural and analogical relations, which image best completes the problem matrix? | E | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
233 | Following the structural and analogical relations, which image best completes the problem matrix? | H | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
234 | Following the structural and analogical relations, which image best completes the problem matrix? | C | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
235 | Following the structural and analogical relations, which image best completes the problem matrix? | D | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
236 | Following the structural and analogical relations, which image best completes the problem matrix? | E | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
237 | Following the structural and analogical relations, which image best completes the problem matrix? | G | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
238 | Following the structural and analogical relations, which image best completes the problem matrix? | E | Choice 0 | Choice 1 | Choice 2 | Choice 3 | Choice 4 | Choice 5 | Choice 6 | Choice 7 | ravens_progressive_matrices | intelligence_quotient_test | VAL | ||
239 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.479, 0.921) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | B | 0 | 176 | 255 | 217 | image_matting | pixel_level_perception | VAL | ||||||
240 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.157, 1.124) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | B | 0 | 4 | 34 | 255 | image_matting | pixel_level_perception | VAL | ||||||
241 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (1.328, 0.342) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | C | 250 | 204 | 255 | 0 | image_matting | pixel_level_perception | VAL | ||||||
242 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.771, 0.557) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | A | 255 | 181 | 0 | 227 | image_matting | pixel_level_perception | VAL | ||||||
243 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.894, 0.28) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 in... | B | 0 | 6 | 102 | 255 | image_matting | pixel_level_perception | VAL | ||||||
244 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.389, 0.688) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | C | 9 | 0 | 34 | 255 | image_matting | pixel_level_perception | VAL | ||||||
245 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (1.327, 0.274) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | B | 255 | 23 | 131 | 0 | image_matting | pixel_level_perception | VAL | ||||||
246 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.252, 1.258) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | A | 255 | 0 | 168 | 127 | image_matting | pixel_level_perception | VAL | ||||||
247 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.973, 0.428) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | B | 219 | 254 | 0 | 255 | image_matting | pixel_level_perception | VAL | ||||||
248 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.379, 0.574) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | A | 107 | 0 | 77 | 255 | image_matting | pixel_level_perception | VAL | ||||||
249 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (1.144, 0.43) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 in... | C | 238 | 101 | 255 | 0 | image_matting | pixel_level_perception | VAL | ||||||
250 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.581, 0.94) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 in... | A | 0 | 42 | 255 | 37 | image_matting | pixel_level_perception | VAL | ||||||
251 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.62, 1.488) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 in... | D | 198 | 112 | 255 | 0 | image_matting | pixel_level_perception | VAL | ||||||
252 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.63, 0.881) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 in... | D | 255 | 177 | 0 | 254 | image_matting | pixel_level_perception | VAL | ||||||
253 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.245, 1.096) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | D | 16 | 170 | 0 | 255 | image_matting | pixel_level_perception | VAL | ||||||
254 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.622, 0.216) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | D | 0 | 10 | 255 | 254 | image_matting | pixel_level_perception | VAL | ||||||
255 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.88, 0.535) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 in... | D | 255 | 43 | 181 | 0 | image_matting | pixel_level_perception | VAL | ||||||
256 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.255, 0.334) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | D | 241 | 255 | 0 | 247 | image_matting | pixel_level_perception | VAL | ||||||
257 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.927, 0.408) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | D | 0 | 255 | 84 | 243 | image_matting | pixel_level_perception | VAL | ||||||
258 | You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.196, 0.894) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 i... | C | 255 | 0 | 254 | 143 | image_matting | pixel_level_perception | VAL | ||||||
259 | What is the depth (in meters) at the coordinates (0.225, 1.125) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617 | C | 1.403 | 0.091 | 0.783 | 0.101 | depth_estimation | pixel_level_perception | VAL | ||||||
260 | What is the depth (in meters) at the coordinates (0.464, 1.144) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617 | B | 2.179 | 3.213 | 5.608 | 4.223 | depth_estimation | pixel_level_perception | VAL | ||||||
261 | What is the depth (in meters) at the coordinates (0.414, 1.21) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617 | D | 5.661 | 0.96 | 3.897 | 3.603 | depth_estimation | pixel_level_perception | VAL | ||||||
262 | What is the depth (in meters) at the coordinates (0.18, 0.715) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | A | 27.1640625 | 19.926 | 53.428 | 31.937 | depth_estimation | pixel_level_perception | VAL | ||||||
263 | What is the depth (in meters) at the coordinates (0.225, 1.259) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | A | 11.56640625 | 17.426 | 20.768 | 11.259 | depth_estimation | pixel_level_perception | VAL | ||||||
264 | What is the depth (in meters) at the coordinates (0.24, 2.88) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | B | 4.458 | 8.50390625 | 4.764 | 12.443 | depth_estimation | pixel_level_perception | VAL | ||||||
265 | What is the depth (in meters) at the coordinates (0.199, 2.096) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | B | 3.413 | 12.7734375 | 2.555 | 9.476 | depth_estimation | pixel_level_perception | VAL | ||||||
266 | What is the depth (in meters) at the coordinates (0.218, 1.465) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | D | 4.312 | 18.945 | 20.441 | 12.02734375 | depth_estimation | pixel_level_perception | VAL | ||||||
267 | What is the depth (in meters) at the coordinates (0.279, 1.661) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | A | 6.80078125 | 2.975 | 7.84 | 5.655 | depth_estimation | pixel_level_perception | VAL | ||||||
268 | What is the depth (in meters) at the coordinates (0.169, 1.472) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | A | 32.703125 | 25.846 | 30.798 | 28.812 | depth_estimation | pixel_level_perception | VAL | ||||||
269 | What is the depth (in meters) at the coordinates (0.211, 2.043) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | D | 3.677 | 0.368 | 22.269 | 11.78125 | depth_estimation | pixel_level_perception | VAL | ||||||
270 | What is the depth (in meters) at the coordinates (0.224, 2.947) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | B | 12.785 | 11.17578125 | 21.288 | 15.106 | depth_estimation | pixel_level_perception | VAL | ||||||
271 | What is the depth (in meters) at the coordinates (0.25, 1.216) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | D | 1.848 | 13.267 | 6.072 | 8.4765625 | depth_estimation | pixel_level_perception | VAL | ||||||
272 | What is the depth (in meters) at the coordinates (0.62, 1.129) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617 | B | 2.002 | 1.279 | 2.213 | 1.074 | depth_estimation | pixel_level_perception | VAL | ||||||
273 | What is the depth (in meters) at the coordinates (0.2, 2.147) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | D | 11.525 | 25.068 | 17.117 | 14.87109375 | depth_estimation | pixel_level_perception | VAL | ||||||
274 | What is the depth (in meters) at the coordinates (0.212, 2.131) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | D | 14.065 | 23.368 | 22.82 | 13.359375 | depth_estimation | pixel_level_perception | VAL | ||||||
275 | What is the depth (in meters) at the coordinates (0.252, 1.936) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | C | 11.501 | 12.198 | 8.7578125 | 0.145 | depth_estimation | pixel_level_perception | VAL | ||||||
276 | What is the depth (in meters) at the coordinates (0.171, 0.792) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | B | 12.081 | 31.93359375 | 18.879 | 13.233 | depth_estimation | pixel_level_perception | VAL | ||||||
277 | What is the depth (in meters) at the coordinates (0.164, 1.315) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | D | 12.6 | 2.697 | 8.668 | 10.3828125 | depth_estimation | pixel_level_perception | VAL | ||||||
278 | What is the depth (in meters) at the coordinates (0.259, 1.181) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854 | C | 4.899 | 14.012 | 7.96875 | 3.777 | depth_estimation | pixel_level_perception | VAL | ||||||
279 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | B | ('train', '450', '217', '113', '182'): [(0.703, 0.339), (0.177, 0.284)], ('train', '450', '217', '113', '182'): [(0.247, 0.836), (0.428, 0.031)] | ('train', '260', '88', '416', '364'): [(0.406, 0.138), (0.65, 0.569)], ('train', '260', '88', '416', '364'): [(0.955, 0.431), (0.369, 0.775)] | ('train', '595', '165', '142', '141'): [(0.93, 0.258), (0.222, 0.22)], ('train', '595', '165', '142', '141'): [(0.766, 0.548), (0.708, 0.723)] | ('train', '277', '211', '135', '62'): [(0.433, 0.33), (0.211, 0.097)], ('train', '277', '211', '135', '62'): [(0.244, 0.669), (0.087, 0.791)] | pixel_localization | pixel_level_perception | VAL | ||||||
280 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | B | ('bus', '21', '285', '328', '134'): [(0.033, 0.594), (0.512, 0.279)], ('bus', '21', '285', '328', '134'): [(0.38, 0.579), (0.377, 0.585)] | ('bus', '202', '369', '410', '381'): [(0.316, 0.769), (0.641, 0.794)], ('bus', '202', '369', '410', '381'): [(0.372, 0.621), (0.034, 1.085)] | ('bus', '356', '420', '244', '281'): [(0.556, 0.875), (0.381, 0.585)], ('bus', '356', '420', '244', '281'): [(0.372, 0.621), (0.034, 1.085)] | ('bus', '215', '424', '240', '298'): [(0.336, 0.883), (0.375, 0.621)], ('bus', '215', '424', '240', '298'): [(0.372, 0.623), (0.383, 0.61)] | pixel_localization | pixel_level_perception | VAL | ||||||
281 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | A | ('clock', '331', '208', '454', '14'): [(0.517, 0.433), (0.709, 0.029)], ('clock', '331', '208', '454', '14'): [(0.483, 1.085), (0.669, 1.304)] | ('clock', '226', '119', '161', '1'): [(0.353, 0.248), (0.252, 0.002)], ('clock', '226', '119', '161', '1'): [(0.359, 1.308), (0.586, 0.458)] | ('clock', '346', '519', '94', '358'): [(0.541, 1.081), (0.147, 0.746)], ('clock', '346', '519', '94', '358'): [(0.616, 0.938), (0.453, 0.423)] | ('clock', '303', '580', '408', '519'): [(0.473, 1.208), (0.637, 1.081)], ('clock', '303', '580', '408', '519'): [(0.731, 0.317), (0.527, 0.456)] | pixel_localization | pixel_level_perception | VAL | ||||||
282 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | B | ('person', '529', '201', '225', '268'): [(1.239, 0.314), (0.527, 0.419)], ('person', '529', '201', '225', '268'): [(1.192, 0.414), (0.475, 0.328)] | ('person', '183', '288', '94', '71'): [(0.429, 0.45), (0.22, 0.111)], ('person', '183', '288', '94', '71'): [(0.602, 0.034), (0.096, 0.584)] | ('person', '357', '78', '456', '114'): [(0.836, 0.122), (1.068, 0.178)], ('person', '357', '78', '456', '114'): [(1.159, 0.031), (0.993, 0.047)] | ('person', '82', '310', '141', '213'): [(0.192, 0.484), (0.33, 0.333)], ('person', '82', '310', '141', '213'): [(0.246, 0.305), (1.227, 0.469)] | pixel_localization | pixel_level_perception | VAL | ||||||
283 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | D | ('airplane', '207', '255', '310', '244'): [(0.323, 0.597), (0.484, 0.571)] | ('airplane', '237', '87', '227', '87'): [(0.37, 0.204), (0.355, 0.204)] | ('airplane', '267', '293', '228', '311'): [(0.417, 0.686), (0.356, 0.728)] | ('airplane', '235', '203', '407', '370'): [(0.367, 0.475), (0.636, 0.867)] | pixel_localization | pixel_level_perception | VAL | ||||||
284 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | D | ('person', '186', '294', '49', '106'): [(0.291, 0.689), (0.077, 0.248)], ('person', '186', '294', '49', '106'): [(0.464, 0.782), (0.481, 0.794)] | ('person', '185', '321', '210', '302'): [(0.289, 0.752), (0.328, 0.707)], ('person', '185', '321', '210', '302'): [(0.005, 0.159), (0.014, 0.384)] | ('person', '280', '550', '62', '570'): [(0.438, 1.288), (0.097, 1.335)], ('person', '280', '550', '62', '570'): [(0.545, 0.363), (0.614, 0.438)] | ('person', '186', '294', '49', '106'): [(0.291, 0.689), (0.077, 0.248)], ('person', '186', '294', '49', '106'): [(0.478, 0.803), (0.059, 0.225)] | pixel_localization | pixel_level_perception | VAL | ||||||
285 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | D | ('cat', '327', '314', '275', '271'): [(0.654, 0.837), (0.55, 0.723)], ('cat', '327', '314', '275', '271'): [(0.476, 0.147), (0.486, 0.885)] | ('cat', '147', '430', '124', '263'): [(0.294, 1.147), (0.248, 0.701)], ('cat', '147', '430', '124', '263'): [(0.008, 0.389), (0.178, 0.304)] | ('cat', '240', '324', '271', '337'): [(0.48, 0.864), (0.542, 0.899)], ('cat', '240', '324', '271', '337'): [(0.28, 0.048), (0.536, 0.832)] | ('cat', '244', '271', '263', '171'): [(0.488, 0.723), (0.526, 0.456)], ('cat', '244', '271', '263', '171'): [(0.252, 0.827), (0.438, 0.44)] | pixel_localization | pixel_level_perception | VAL | ||||||
286 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | C | ('bird', '408', '173', '282', '409'): [(0.637, 0.338), (0.441, 0.799)] | ('bird', '190', '434', '112', '191'): [(0.297, 0.848), (0.175, 0.373)] | ('bird', '325', '464', '87', '466'): [(0.508, 0.906), (0.136, 0.91)] | ('bird', '41', '360', '167', '92'): [(0.064, 0.703), (0.261, 0.18)] | pixel_localization | pixel_level_perception | VAL | ||||||
287 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | D | ('bear', '409', '279', '168', '201'): [(0.639, 0.653), (0.263, 0.471)] | ('bear', '263', '138', '373', '163'): [(0.411, 0.323), (0.583, 0.382)] | ('bear', '43', '268', '208', '82'): [(0.067, 0.628), (0.325, 0.192)] | ('bear', '265', '95', '57', '45'): [(0.414, 0.222), (0.089, 0.105)] | pixel_localization | pixel_level_perception | VAL | ||||||
288 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | A | ('tie', '627', '226', '97', '261'): [(1.472, 0.353), (0.228, 0.408)], ('tie', '627', '226', '97', '261'): [(0.258, 0.548), (0.009, 0.655)], ('tie', '627', '226', '97', '261'): [(1.446, 0.123), (0.869, 0.603)] | ('tie', '267', '183', '249', '291'): [(0.627, 0.286), (0.585, 0.455)], ('tie', '267', '183', '249', '291'): [(0.953, 0.492), (1.408, 0.25)], ('tie', '267', '183', '249', '291'): [(1.392, 0.147), (1.46, 0.169)] | ('tie', '525', '226', '549', '216'): [(1.232, 0.353), (1.289, 0.338)], ('tie', '525', '226', '549', '216'): [(0.345, 0.236), (0.042, 0.114)], ('tie', '525', '226', '549', '216'): [(1.369, 0.173), (1.472, 0.119)] | ('tie', '584', '216', '504', '239'): [(1.371, 0.338), (1.183, 0.373)], ('tie', '584', '216', '504', '239'): [(0.103, 0.603), (0.101, 0.661)], ('tie', '584', '216', '504', '239'): [(1.484, 0.097), (0.915, 0.188)] | pixel_localization | pixel_level_perception | VAL | ||||||
289 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | C | ('cow', '77', '129', '422', '403'): [(0.126, 0.211), (0.69, 0.658)], ('cow', '77', '129', '422', '403'): [(0.693, 0.663), (0.699, 0.582)], ('cow', '77', '129', '422', '403'): [(0.327, 0.843), (0.498, 0.358)] | ('cow', '65', '77', '157', '206'): [(0.106, 0.126), (0.257, 0.337)], ('cow', '65', '77', '157', '206'): [(0.252, 0.773), (0.404, 0.003)], ('cow', '65', '77', '157', '206'): [(0.258, 0.391), (0.374, 0.523)] | ('cow', '355', '252', '541', '28'): [(0.58, 0.412), (0.884, 0.046)], ('cow', '355', '252', '541', '28'): [(0.252, 0.773), (0.404, 0.003)], ('cow', '355', '252', '541', '28'): [(0.258, 0.391), (0.374, 0.523)] | ('cow', '63', '356', '161', '210'): [(0.103, 0.582), (0.263, 0.343)], ('cow', '63', '356', '161', '210'): [(0.252, 0.773), (0.404, 0.003)], ('cow', '63', '356', '161', '210'): [(0.248, 0.355), (0.243, 0.381)] | pixel_localization | pixel_level_perception | VAL | ||||||
290 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | C | ('tie', '228', '170', '248', '174'): [(0.592, 0.266), (0.644, 0.272)], ('tie', '228', '170', '248', '174'): [(1.018, 0.402), (1.501, 0.103)], ('tie', '228', '170', '248', '174'): [(1.101, 0.098), (1.358, 0.395)] | ('tie', '13', '297', '423', '208'): [(0.034, 0.464), (1.099, 0.325)], ('tie', '13', '297', '423', '208'): [(0.27, 0.028), (0.735, 0.259)], ('tie', '13', '297', '423', '208'): [(1.021, 0.109), (0.766, 0.258)] | ('tie', '208', '164', '2', '104'): [(0.54, 0.256), (0.005, 0.163)], ('tie', '208', '164', '2', '104'): [(1.018, 0.402), (1.501, 0.103)], ('tie', '208', '164', '2', '104'): [(1.101, 0.098), (1.358, 0.395)] | ('tie', '344', '250', '213', '166'): [(0.894, 0.391), (0.553, 0.259)], ('tie', '344', '250', '213', '166'): [(0.87, 0.438), (0.621, 0.391)], ('tie', '344', '250', '213', '166'): [(0.091, 0.4), (1.512, 0.369)] | pixel_localization | pixel_level_perception | VAL | ||||||
291 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | C | ('clock', '607', '250', '620', '157'): [(1.432, 0.391), (1.462, 0.245)] | ('clock', '345', '308', '242', '218'): [(0.814, 0.481), (0.571, 0.341)] | ('clock', '241', '203', '201', '197'): [(0.568, 0.317), (0.474, 0.308)] | ('clock', '247', '216', '238', '205'): [(0.583, 0.338), (0.561, 0.32)] | pixel_localization | pixel_level_perception | VAL | ||||||
292 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | A | ('person', '261', '101', '152', '414'): [(0.522, 0.269), (0.304, 1.104)], ('person', '261', '101', '152', '414'): [(0.314, 1.168), (0.004, 0.285)] | ('person', '212', '138', '298', '343'): [(0.424, 0.368), (0.596, 0.915)], ('person', '212', '138', '298', '343'): [(0.314, 1.168), (0.004, 0.285)] | ('person', '97', '122', '160', '480'): [(0.194, 0.325), (0.32, 1.28)], ('person', '97', '122', '160', '480'): [(0.58, 1.072), (0.71, 1.091)] | ('person', '348', '304', '299', '228'): [(0.696, 0.811), (0.598, 0.608)], ('person', '348', '304', '299', '228'): [(0.664, 0.864), (0.64, 1.235)] | pixel_localization | pixel_level_perception | VAL | ||||||
293 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | A | ('banana', '457', '222', '13', '110'): [(1.068, 0.347), (0.03, 0.172)] | ('banana', '615', '99', '611', '98'): [(1.437, 0.155), (1.428, 0.153)] | ('banana', '308', '426', '85', '303'): [(0.72, 0.666), (0.199, 0.473)] | ('banana', '489', '399', '378', '404'): [(1.143, 0.623), (0.883, 0.631)] | pixel_localization | pixel_level_perception | VAL | ||||||
294 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | C | ('person', '235', '101', '271', '86'): [(0.367, 0.21), (0.423, 0.179)], ('person', '235', '101', '271', '86'): [(0.386, 0.108), (0.333, 0.173)], ('person', '235', '101', '271', '86'): [(0.498, 1.242), (0.287, 0.992)] | ('person', '248', '80', '299', '94'): [(0.388, 0.167), (0.467, 0.196)], ('person', '248', '80', '299', '94'): [(0.344, 0.629), (0.333, 0.61)], ('person', '248', '80', '299', '94'): [(0.566, 1.219), (0.602, 0.071)] | ('person', '215', '94', '343', '529'): [(0.336, 0.196), (0.536, 1.102)], ('person', '215', '94', '343', '529'): [(0.339, 0.617), (0.147, 0.006)], ('person', '215', '94', '343', '529'): [(0.498, 1.242), (0.287, 0.992)] | ('person', '225', '74', '272', '109'): [(0.352, 0.154), (0.425, 0.227)], ('person', '225', '74', '272', '109'): [(0.336, 0.61), (0.334, 0.631)], ('person', '225', '74', '272', '109'): [(0.659, 0.577), (0.328, 0.627)] | pixel_localization | pixel_level_perception | VAL | ||||||
295 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | B | ('dog', '341', '181', '279', '247'): [(0.533, 0.377), (0.436, 0.515)], ('dog', '341', '181', '279', '247'): [(0.444, 0.287), (0.398, 0.34)], ('dog', '341', '181', '279', '247'): [(0.286, 0.456), (0.209, 1.137)] | ('dog', '195', '504', '212', '113'): [(0.305, 1.05), (0.331, 0.235)], ('dog', '195', '504', '212', '113'): [(0.397, 0.315), (0.523, 1.265)], ('dog', '195', '504', '212', '113'): [(0.286, 0.456), (0.209, 1.137)] | ('dog', '355', '108', '229', '274'): [(0.555, 0.225), (0.358, 0.571)], ('dog', '355', '108', '229', '274'): [(0.503, 0.6), (0.289, 0.444)], ('dog', '355', '108', '229', '274'): [(0.733, 0.106), (0.27, 0.433)] | ('dog', '148', '626', '35', '558'): [(0.231, 1.304), (0.055, 1.163)], ('dog', '148', '626', '35', '558'): [(0.303, 0.254), (0.372, 0.26)], ('dog', '148', '626', '35', '558'): [(0.286, 0.456), (0.209, 1.137)] | pixel_localization | pixel_level_perception | VAL | ||||||
296 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | D | ('vase', '537', '142', '469', '258'): [(1.119, 0.222), (0.977, 0.403)], ('vase', '537', '142', '469', '258'): [(0.696, 0.042), (1.171, 0.15)] | ('vase', '286', '237', '493', '84'): [(0.596, 0.37), (1.027, 0.131)], ('vase', '286', '237', '493', '84'): [(0.802, 0.359), (0.854, 0.403)] | ('vase', '234', '341', '44', '145'): [(0.487, 0.533), (0.092, 0.227)], ('vase', '234', '341', '44', '145'): [(0.863, 0.256), (0.521, 0.191)] | ('vase', '286', '237', '493', '84'): [(0.596, 0.37), (1.027, 0.131)], ('vase', '286', '237', '493', '84'): [(0.746, 0.38), (0.438, 0.128)] | pixel_localization | pixel_level_perception | VAL | ||||||
297 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | B | ('person', '28', '10', '158', '170'): [(0.056, 0.027), (0.316, 0.453)], ('person', '28', '10', '158', '170'): [(0.558, 0.597), (0.324, 0.352)] | ('person', '56', '153', '212', '353'): [(0.112, 0.408), (0.424, 0.941)], ('person', '56', '153', '212', '353'): [(0.316, 0.405), (0.322, 0.293)] | ('person', '162', '371', '112', '316'): [(0.324, 0.989), (0.224, 0.843)], ('person', '162', '371', '112', '316'): [(0.714, 0.621), (0.158, 0.373)] | ('person', '56', '153', '212', '353'): [(0.112, 0.408), (0.424, 0.941)], ('person', '56', '153', '212', '353'): [(0.008, 1.256), (0.328, 0.424)] | pixel_localization | pixel_level_perception | VAL | ||||||
298 | Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee... | C | ('vase', '230', '240', '237', '265'): [(0.46, 0.704), (0.474, 0.777)], ('vase', '230', '240', '237', '265'): [(0.098, 1.085), (0.596, 0.587)] | ('vase', '327', '248', '318', '204'): [(0.654, 0.727), (0.636, 0.598)], ('vase', '327', '248', '318', '204'): [(0.618, 0.211), (0.53, 0.613)] | ('vase', '310', '238', '128', '26'): [(0.62, 0.698), (0.256, 0.076)], ('vase', '310', '238', '128', '26'): [(0.076, 0.584), (0.448, 0.223)] | ('vase', '310', '238', '128', '26'): [(0.62, 0.698), (0.256, 0.076)], ('vase', '310', '238', '128', '26'): [(0.272, 1.062), (0.136, 0.32)] | pixel_localization | pixel_level_perception | VAL | ||||||
299 | What is the semantic category of the pixel point at coordinates (0.473, 0.222) in the image? Note that the width of the input image is given as 640 and the height as 427. The coordinates of the top left corner of the image are (0, 0), and the coordinates of the bottom right corner are (640, 427). | D | motorcycle | bus | car | truck | pixel_recognition | pixel_level_perception | VAL |
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